Key Responsibilities and Required Skills for Enterprise Analytics Service Lead

🎯 Role Definition

The Enterprise Analytics Service Lead owns the end-to-end analytics service offering across the organization — including platform strategy, platform engineering collaboration, governance, service delivery, adoption, performance, and cost optimization. This leader partners with business stakeholders, analytics engineering, data science, IT/cloud, and security teams to deliver trusted, scalable, and user-friendly analytics solutions that accelerate insight-driven decision-making.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Analytics Manager or BI Manager with enterprise platform responsibility
Data Platform Product Manager / Analytics Platform Manager
Head of Business Intelligence or Analytics Engineering

Advancement To:

Director, Data & Analytics Platforms
Head of Enterprise Analytics / Global Analytics Lead
VP of Data, Analytics & Insights or Chief Data Officer

Lateral Moves:

Data Product Manager (Analytics Products)
Analytics Consulting Lead / Principal Analytics Architect
Data Governance or Data Strategy Lead

Core Responsibilities

Primary Functions

Own the enterprise analytics service roadmap and backlog, translating business priorities into a multi-year plan that aligns platform investments (BI tools, data warehouse, Lakehouse, ETL/ELT pipelines) with executive strategy and measurable adoption KPIs.
Lead the design, delivery, and operation of a secure, scalable analytics platform (including cloud data warehouse such as Snowflake/Redshift, Databricks, BI tools like Power BI/Tableau, and orchestration) to ensure high availability, scalability, and cost efficiency for all analytics consumers.
Establish and enforce analytics service-level agreements (SLAs), operational runbooks, incident response processes, and performance monitoring to meet uptime, query performance, and request turnaround targets for business stakeholders.
Serve as the primary business-facing leader for analytics service delivery, building strong relationships with business unit leaders, prioritizing requests, mediating resource trade-offs, and ensuring analytics outcomes map to business value and measurable metrics.
Drive adoption of self-service analytics by defining a service catalog, user personas, enablement programs, templated data models, and governed sandboxes that empower analysts while maintaining data governance and security.
Define and implement enterprise data governance and metadata management practices in collaboration with data governance and data engineering teams, including data cataloging, lineage, classification, and stewardship models that enable trust and compliance.
Lead a cross-functional team of analytics engineers, data architects, platform engineers, and vendor partners; recruit, coach, and develop talent while creating a high-performing culture focused on delivery, quality, and stakeholder satisfaction.
Oversee cost management and capacity planning for analytics infrastructure (compute, storage, BI licensing), implementing tagging, budget controls, workload management, and optimization practices to reduce waste and forecast spend.
Partner with cloud and platform engineering to drive infrastructure-as-code, CI/CD pipelines for analytics artifacts (SQL, models, dashboards), automated testing, and deployment processes to reduce production risk and accelerate delivery cycles.
Standardize data modeling approaches, canonical enterprise data sets, dimensional models and metrics layer (semantic layer) to ensure consistent KPIs, single source of truth reporting, and reusable analytical assets across business functions.
Lead analytics platform vendor and tool evaluations, procurements, contract negotiations, SLAs and ongoing vendor management to ensure best-fit solutions are selected and ROI is delivered.
Implement security, privacy, and compliance controls in the analytics stack including access controls, PII masking, data retention, and audit logging to meet regulatory and internal policy requirements.
Define and measure analytics service health and value through metrics like time-to-insight, dashboard adoption, query latency, data freshness, user satisfaction (NPS), and business impact attribution.
Provide executive-level reporting and storytelling on analytics performance, roadmap progress, risk, and value realization to CIO, CDO, and business leaders to secure ongoing investment and alignment.
Manage intake, prioritization and delivery of analytics requests through a transparent governance process — including business case evaluation, benefits estimation, and resource allocation decisions.
Drive data literacy and enablement programs (training, office hours, documentation, developer playbooks) to increase analytics maturity and self-service capabilities across the enterprise.
Lead cross-functional programs to decommission legacy reporting, migrate dashboards and datasets to modern platforms, and consolidate reporting silos to reduce technical debt and duplication.
Act as the accountability owner for analytics change management by coordinating release plans, stakeholder communication, engagement plans, and adoption metrics for major platform changes.
Establish monitoring and observability for analytics pipelines, dashboards, and critical ETL/ELT jobs; create alerting thresholds, escalation paths, and remediation playbooks to minimize business disruption.
Drive collaboration with data science teams to productionize models and embed ML/advanced analytics outputs into repeatable analytics services, ensuring model governance and monitoring.
Champion continuous improvement by defining best practices, conducting post-implementation reviews, and iterating on service processes to increase velocity, quality, and user satisfaction.
Facilitate cross-functional architecture decisions and ensure alignment to enterprise data architecture, master data management, and integration patterns to reduce duplication and improve interoperability.
Coordinate and manage analytics platform releases, upgrades, and patching (BI tools, connectors, cloud services), ensuring compatibility, security, and minimal disruption to users.
Support regulatory and audit requirements by providing evidence of analytics governance, data lineage, access logs and demonstrating controlled processes for data handling within the analytics environment.
Build and maintain an internal pricing and cost-allocation model for analytics consumption to promote responsible usage and transparency across business units.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL (query optimization, window functions, complex joins) for analytics engineering and troubleshooting production queries.
Expertise with cloud data warehouses and Lakehouse technologies (Snowflake, AWS Redshift, Google BigQuery, Databricks) and schema design at scale.
Hands-on experience with BI and visualization platforms (Power BI, Tableau, Looker, Qlik) including governance, distribution, and performance tuning.
Proficiency with ETL/ELT tools and orchestration frameworks (Fivetran, Stitch, Airflow, dbt) and experience implementing CI/CD for analytics artifacts.
Knowledge of cloud platforms and services (AWS, Azure, GCP) and their analytics ecosystems (S3, Glue, Synapse, Redshift Spectrum, Azure Databricks).
Familiarity with analytics semantic layers, metric stores, data models (star schema, fact/dimension modeling) and implementing a centralized metrics layer.
Experience with data governance tools and concepts: data cataloging, data lineage (OpenLineage), data classification, access controls and policy enforcement.
Competence in scripting and programming (Python, R) for ETL, lightweight transformation, automation, and integration with data science workflows.
Understanding of security, compliance and privacy controls (encryption, IAM, GDPR, CCPA) as applied to analytics data and pipelines.
Strong capability in platform cost management and observability tools (monitoring, logging, query profiling, usage analytics).
Familiarity with MLOps practices and ability to collaborate on model deployment, monitoring, and lifecycle management.

Soft Skills

Strategic thinking and business acumen to align analytics services with organizational goals and demonstrate clear ROI.
Exceptional stakeholder management and communication skills, with the ability to present technical concepts and analytics outcomes to executive audiences.
Proven leadership and people-management skills: hiring, mentoring, performance management, and developing cross-functional teams.
Influencing and negotiation skills to prioritize work across competing business demands and collaborate with vendor partners.
Strong problem-solving orientation, able to troubleshoot complex production incidents and lead root-cause analysis.
Change management and coaching skills to drive adoption, increase analytics maturity, and shift cultural behaviors toward data-driven decision making.
Project and program management discipline, including scoping, backlog management, risk mitigation, and delivery tracking.
Customer-service mindset focused on delivering measurable value and building trust with analytics consumers.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Information Systems, Business Analytics or related field.

Preferred Education:

Master’s degree in Data Science, Business Analytics, Computer Science, or an MBA with analytics concentration.

Relevant Fields of Study:

Data Science / Machine Learning
Computer Science / Software Engineering
Statistics / Applied Mathematics
Information Systems / Business Analytics
Cloud Computing / Big Data Technologies

Experience Requirements

Typical Experience Range: 7–12 years of progressive experience in analytics, BI, data engineering or platform roles with at least 3–5 years in a leadership position owning enterprise analytics services.

Preferred:

10+ years designing and operating enterprise analytics platforms for large, matrixed organizations; proven track record of migrating legacy reporting to modern cloud-first stacks.
Demonstrated experience managing cross-functional teams, vendor relationships, and multi-million dollar platform budgets.
Experience implementing governance and self-service programs that measurably increased adoption and reduced time-to-insight.